This paper proposes a new information-theoretic method based on the information enhancement
method to extract important input variables. The information enhancement
method was developed to detect important components in neural systems. Previous methods
have focused on the detection of only the most important components, and therefore
have failed to fully incorporated the information contained in the components into
learning processes. In addition, it has been observed that the information enhancement
method cannot always extract input information from input patterns. Thus, in this paper
a computational method is developed to accumulate information content in the process of
information enhancement. The method was applied to an artificial data set and the analysis
of mission statements. The results demonstrate that while we were able to explicitly
extract the symmetric properties of the data from the artificial data set, only one main
factor was able to be extracted from the mission statement, namely, “contribution to the
society”. The companies with higher profits tend to have mission statements concerning
the society. The results can be considered to be a first step toward the full clarification of
the importance of mission statements in actual business activities.
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